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Function Signature

def print_metrics(metrics: Dict[str, Any]) -> None

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Description

The print_metrics() function displays model evaluation metrics in a clean, formatted table. It automatically detects potential overfitting or underfitting issues and provides visual indicators.

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Parameters

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metrics
Dict[str, Any]
required
A dictionary containing model evaluation metrics, typically returned by the evaluate_model() function.Required keys:
  • model_name (str): Name of the model
  • train_mse (float): Training Mean Squared Error
  • test_mse (float): Test Mean Squared Error
  • train_rmse (float): Training Root Mean Squared Error
  • test_rmse (float): Test Root Mean Squared Error
  • train_mae (float): Training Mean Absolute Error
  • test_mae (float): Test Mean Absolute Error
  • train_r2 (float): Training R² score
  • test_r2 (float): Test R² score
  • cv_r2_mean (float): Cross-validation R² mean
  • cv_r2_std (float): Cross-validation R² standard deviation

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Return Value

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return
None
This function does not return a value. It prints formatted output to the console.

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Output Format

The function prints a formatted table showing:
  1. Model Name Header - Clearly identifies the model being evaluated
  2. Performance Metrics - Side-by-side comparison of training vs test metrics:
    • MSE (Mean Squared Error)
    • RMSE (Root Mean Squared Error)
    • MAE (Mean Absolute Error)
    • R² (Coefficient of Determination)
  3. Cross-Validation Results - Mean ± standard deviation of CV R² scores
  4. Model Fit Analysis - Automatic detection of overfitting or underfitting

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Example Output

==================================================
Model: Linear Regression (Multivariate)
==================================================
Train MSE: 22.5631 | Test MSE: 21.6191
Train RMSE: 4.7508 | Test RMSE: 4.6496
Train MAE: 3.4521 | Test MAE: 3.3874
Train R²: 0.7432 | Test R²: 0.7099
CV R² (mean±std): 0.6880 ± 0.0521
✅ Good fit

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Overfitting and Underfitting Detection

The function automatically analyzes the metrics and provides warnings:

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Usage Example

from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
import pandas as pd

# Prepare data
df = pd.read_csv('house_prices.csv')
X = df[['area', 'bedrooms', 'bathrooms']]
y = df['price']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train and evaluate model
model = DecisionTreeRegressor(max_depth=10, random_state=42)
model.fit(X_train, y_train)

metrics, predictions = evaluate_model(
    model, X_train, X_test, y_train, y_test, 
    "Decision Tree Regression"
)

# Print formatted metrics
print_metrics(metrics)
Output: 
==================================================
Model: Decision Tree Regression
==================================================
Train MSE: 6.3556 | Test MSE: 11.2138
Train RMSE: 2.5206 | Test RMSE: 3.3487
Train MAE: 1.8234 | Test MAE: 2.4521
Train R²: 0.9277 | Test R²: 0.8495
CV R² (mean±std): 0.7239 ± 0.0891
✅ Good fit

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Use Cases

  1. Quick Model Assessment - Instantly see if your model is performing well
  2. Debugging - Identify overfitting/underfitting issues during development
  3. Model Comparison - Print metrics for multiple models to compare visually
  4. Reporting - Generate formatted output for documentation or reports

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Integration with Workflow

# Typical workflow
models = [
    ('Linear Regression', LinearRegression()),
    ('Decision Tree', DecisionTreeRegressor(max_depth=10)),
    ('Neural Network', MLPRegressor(hidden_layer_sizes=(100, 50)))
]

for name, model in models:
    model.fit(X_train, y_train)
    metrics, _ = evaluate_model(model, X_train, X_test, y_train, y_test, name)
    print_metrics(metrics)  # Print each model's metrics
    
    # Save metrics for later comparison
    with open(f'results/metrics_{name}.json', 'w') as f:
        json.dump(metrics, f, indent=2)

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